The field of the disclosure relates generally to surveillance data analysis, and more specifically, to methods and systems for estimating subject intent from surveillance.
Analysis of surveillance data is a major bottleneck in improving the situational awareness in security applications. Such security applications can be considered to range from public space surveillance to war theatre surveillance operations. Specifically, there is an abundance of video data available to consumers of such data, many times more than there are available man-hours to watch such video data. Automating analysis tasks is therefore a highly desirable goal. Among the tasks of an intelligence analyst is the estimation of what an observable agent (a person, or, by extension, a vehicle) intends to do, based on its previous behavior. Such observation is typically from video. While the above appears to be directed to a military intelligence context, other related contexts are contemplated.
The above described problem in the automating of the analysis tasks is challenging because the space of possible utility functions is very large. The essence of a successful application of an automated analysis method lies in devising a-priori value function representations that enable a compact representation of the large function space so that the computations become tractable.
The problem can be cast in terms of utility determination. One related approach is termed “Inverse Reinforcement Learning” in machine learning and artificial intelligence literature. Rather, work in Inverse Reinforcement Learning work typically has the goal of repeating a behavior shown in a set of examples typically performed by a person.